About AI with Alec. Get smarter on AI. The easy way.
Conversations with leading technical minds in Artificial Intelligence - from CTOs of pioneering AI startups to AI architects at Fortune 500 companies. We explore their strategies, implementations, and innovations to help you better understand and deploy AI in the real world. Hear enterprise AI insights and practical perspectives you won't find anywhere else. If you're a technical leader, business executive, AI practitioner, or innovation strategist, this is for you.
Does Your Enterprise AI Have an Opinion? Joe Bradley, CTO of IDC | AI with Alec E33
“Does it have an opinion?”
LLMs are trained to be persuadable because persuadable is adjacent to useful. So they drift toward the answer you consciously or subconsciously want.
Every CEO has felt an AI agree with them too early and too eagerly. Every engineer knows exactly why it happens.
Naming it is at the foundation of how you graft AI into your business. Not naming it is how you don’t.
That question came from Joe Bradley, CTO of IDC, on AIWA E33.
IDC built a product, IDC Quanta, to resist that drift by design.
Opinionated and Open:
Joe and his team started where they could close a loop.A “partitionable” business with its own revenue line, its own customer and an outcome you can measure.
Then they evolved it from a pre-ChatGPT era business into an AI-first one.
One that wasn’t distracted by AI for AI’s sake. One focused on IDC’s alpha and its core, differentiated value proposition.
One that gets better as models get better. Not one rendered obsolete.
Not a chatbot. An intelligent system that “escaped the confines of its application” to meet people where they are.
Not a sycophant, an intelligence layer with a POV rooted in 60 years of IDC research.
Not dogmatic. “Opinionated and open.” Strong opinions loosely held meets foundational, research driven truths.
Two things can be true at the same time. It depends on the lens you’re using.
This is context and context is king.
What makes IDC Quanta intriguing is the balance. Preserving the integrity of IDC’s positions while letting the intelligence layer be interrogated and stress tested against alternative POVs, including their clients’.
The Mirror or the Moat:
No surprise IDC picked up on this. Their research is about perspective, knowledge and being right.
Most companies won’t catch the drift in the moment. They’ll catch it in the outcome. Eventually.
Regardless of your industry, encoding your POV is where it starts. You have to defend and evolve it.
Encoding it is a build task. Defending it is an operating discipline, because the model’s eagerness to please never ends.
As I wrote in June, an SME can catch the sycophant. That doesn’t scale. You can’t make every SME, analyst or other leader your last line of defense.
Resistance that lives only in your best people is inconsistent, unauditable and walks out the door the day they leave.
An intelligence layer that agrees with whoever is typing isn’t a moat. It’s a mirror with a search index.
And the defense is what makes the compounding worth having. A system that drifts toward every user compounds noise. One that holds its ground compounds judgement, at scale.
Which leaves one question. How do you know it held?
That’s evals, grounded in your context graph.
Encode the POV. Defend it. Evolve it.
The discipline is the floor. The art is the ceiling.
Joe Bradley and I get into all of it in AIWA E33.
Humans + Machines. Never Humans vs. Machines.
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0:00 Intro: opera, physics and the road to CTO
1:42 CTO vs CIO vs Chief AI Officer
4:40 What "using AI well" actually means
7:34 Where IDC started: the Marketscape
9:43 From internal tool to IDC Quanta
11:01 "I just want a chatbot": chat system vs intelligence layer
14:42 Escaping the confines of the application
15:59 Does it have an opinion?
17:35 Trained to be persuadable, designed to resist
19:49 Opinionated and open: living with multiple truths
21:27 Open source vs closed, saturation and the edge
25:13 Two clocks: the harness and how we build now
29:40 Shared agentic presences and the PM agent
31:20 Who thrives with AI: the ownership model
34:20 The next six months: the data layer and unevenness
37:07 Why we're terrible at predicting the future
39:18 Carve away everything that isn't the sculpture
5 Aug 2026
Raleigh Founder Turning "Show Me, Don't Tell Me" Into AI Podcast + Building for Real | David Shaner
My man David Shaner had me the moment he described his podcast, [REDACTED], featuring entrepreneurs showing what they've built, the scar tissue they developed and the things they learned along the way.The things you can't teach but you most definitely can learn.The things you can only learn by doing.The feel. The intuition. The taste.The stuff that lets you see around corners.Show me, don't tell me. Not the smoke aka the "everything is perfect" posts on social. The hands in the dirt stuff I can't get enough of.David built a startup from scratch to 34 people, then wound it down to 2.Here's the hook. When the people left, the scaffolding stayed. Every process that created value was still mapped. So what did AI let him do with 2 people, standing on the structure built for 34?Our conversation on AIWA E32 was full of hard hitting, founder-led insights including:1: AI is very good at imitating what humans have already figured out that creates value. It's very bad at finding value from scratch. The scaffolding was the container it could imitate.2: Founders know "deep in your subconscious" that half of what they do every day is extraneous. The hard part isn't knowing. It's forcing yourself to prove it.3: The fastest path to getting your hands dirty is to start small and specific, using AI to solve your own problems, personally or professionally.Link to [REDACTED]: https://www.tweenertimes.com/s/redacted?utm_source=substack&utm_medium=menu
11 May 2026
Are Your AI Agents Reasoning From a Snapshot That Never Existed? | Tacnode Founder + CEO
Could Tacnode be the next Databricks? Yes.
Imagine the world before writing. Before books. People had to learn everything from scratch.Thanks to writing, thanks to books, knowledge compounded. Everyone could learn from each other.
Today's AI agents don't yet have their version of writing or books. They operate in isolation, without the shared understanding or context of what previous agents and humans have experienced.
They have to start over. Learn everything from scratch.This is the context gap.Tacnode exists to solve the context gap.
I interviewed Xiaowei Jiang, Tacnode founder + CEO on AI with Alec E31.
What follows are four of his arguments that have stuck with me, worth weighing in your own context.
1: The primary user of enterprise software is shifting from humans to agents.
Databricks. $5.4B revenue run-rate. 65% YoY growth. $134B valuation. 60%+ of the Fortune 500. The most valuable private enterprise software company in the world.That's the bar.In Xiaowei's view, lakehouse architecture became the standard because humans were the primary consumer of data. Databricks and Snowflake built extraordinary capabilities for the analytical workloads they were designed to serve.But humans are slow. A handful of decisions a day with long gaps. Pipelines had time to catch up. Caches had time to refresh.Agents collapse those assumptions. Thousands of decisions a second. No human in the loop. Zero tolerance for conflicting signals.This isn't about replacing analytical platforms. Tacnode sits alongside the lakehouse, purpose-built for real-time agent decisions.
2: Most "AI failures" are not model failures. They are context failures.
Xiaowei broke it into three patterns. AI invents things when context is missing. AI contradicts itself when sources conflict. AI commits confidently to information that is no longer true.The model is rarely the bottleneck. The pipeline behind it is.When an agent reads account balance from one system, transaction velocity from another, and behavior signals from a third, each with its own lag, the model is reasoning from "a snapshot that never existed in the world."In fraud detection and credit underwriting, that fictional snapshot shows up on the P&L.
3: The design starts with what must be true at decision time.
The intuitive approach is to wire together best-in-class components. A great stream processor. A great feature store. A great search engine. Each one correct in isolation. The composite system is not.Guarantees that hold inside one system erode the moment you cross to another.Xiaowei's team inverted the design. Start with what must be true at decision time. Build everything else on top of that contract.This is what first principles looks like below the waterline.
4: Shared context compounds. Isolation does not.
"If a database gives you application shared state, context lake is going to give agents shared memory in a compounding system."
Read that twice.
Every decision an agent makes generates a signal worth keeping. A fraud pattern. A predictive signal. A route cause. In an isolated stack, that learning evaporates with the session. In a Context Lake, it becomes every other agent's capability instantly.The early movers don't just deploy infrastructure. They accumulate institutional knowledge inside it."The cost of waiting is not linear. Every month you wait, the gap grows."
Early movers compound. Late movers start at zero.
Humans + Machines. Never Humans vs. Machines.
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